Hi，folks, Simbase v0.1.0-beta1 just release! We had fix many bugs，and the system are very stable for almost half a year in our cases。
In the docs, we add simple scenario for your references Setup > bmk b2048 t1 t2 t3 ... t2047 t2048 > vmk b2048 article > vmk b2048 userprofile > rmk userprofile article cosinesq Fill data > vadd article 1 0.11 0.112 0.1123... > vadd article 2 0.21 0.212 0.2123... ... > vadd userprofile 1 0.11 0.112 0.1123... > vadd userprofile 2 0.21 0.212 0.2123... ... Query > rrec userprofile 2 article On Sun, Jan 26, 2014 at 9:21 AM, Mingli Yuan <mingli.y...@gmail.com> wrote: > Hi, folks, > > This week we released v0.1.0-alpha3 > > * Remove constrains on vectors, Simbase support arbitrary vectors now > * Fix various bugs on memory structure to keep scale ratio linearly > * Almost 7 times improvement on performance, right now it can handle 100k > dimensional dense vectors in under 0.14 sec on a i7-cup mac laptop. > > From now on, it enter the beta phase. If it is relevant to your work, we > encourage you to have a try, and help us to find more bugs. > > Regards, > Mingli > > > On Mon, Jan 13, 2014 at 5:55 PM, Mingli Yuan <mingli.y...@gmail.com> > wrote: > >> Hi, folks, >> >> We just release an alpha version of Simbase, a vector similarity database >> that talks redis protocol. Since it is the first version of all its >> releases, we decided to keep it in alpha right now, for we want to hear >> from the community for any comments and improvements. >> >> Github page >> ------------------ >> >> https://github.com/guokr/simbase >> >> We introduce the basic idea, limitations, build process and commands >> there. >> >> Background >> ------------------ >> >> Simbase is a tool we developed during the process we revise our content >> recommendation engine. >> >> Our document set have 300k docs, and we use LDA to change them into >> vectors. But how to compare the 300k vectors was a problem for us then. We >> had tried different method, but the performance is not very good. >> >> Since the comparison logic is quit simple, we decided to write a new data >> store to do the tricks. >> >> So far, we are satisfied by its performance. Under the setting of an i7 >> MacBook and 120k 1k-dimensional vector set: >> >> - write: about 1 ops per second >> - read: up to 1k ops per second >> >> The real read performance may be higher than the current result, because >> our testing method is limited. >> >> Regards, >> >> Mingli >> >> >> >> >> > -- You received this message because you are subscribed to the Google Groups "Clojure" group. To post to this group, send email to clojure@googlegroups.com Note that posts from new members are moderated - please be patient with your first post. To unsubscribe from this group, send email to clojure+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/clojure?hl=en --- You received this message because you are subscribed to the Google Groups "Clojure" group. To unsubscribe from this group and stop receiving emails from it, send an email to clojure+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
